Manifold aware discriminant collaborative graph embedding for face recognition

Songjiang Lou; Yanghui Ma; Xiaoming Zhao

doi:10.1117/12.2503280

9 August 2018 Manifold aware discriminant collaborative graph embedding for face recognition

Songjiang Lou, Yanghui Ma, Xiaoming Zhao

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061T (2018) https://doi.org/10.1117/12.2503280
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

Dimensionality reduction has been widely used to deal with high dimensional data. In this paper, based on manifold learning and collaborative representation, an efficient subspace learning algorithm named Manifold Aware Discriminant Collaborative Graph Embedding (MADCGE), is proposed for face recognition. Firstly, the representation coefficients of face images are obtained by collaborative representation combined with label information and manifold structure. Then, it constructs a new graph with the coefficients obtained as the adjacent weights. Lastly, graph embedding is exploited to learn an optimal projective matrix for feature extraction. As a result, the proposed algorithm avoids choosing the neighborhood size of graph, which is difficult in literature. More importantly, it can not only preserve the linear reconstructive relationships between samples, but also sufficiently utilize the merits of label information and nonlinear manifold structure to further improve the discriminative ability. Extensive experiments on face databases (AR face database and YALE-B face database) are conducted to exam the performance of the proposed scheme and the results demonstrate that the proposed method has better performance than some other used methods.

Citation Download Citation

Songjiang Lou, Yanghui Ma, and Xiaoming Zhao "Manifold aware discriminant collaborative graph embedding for face recognition", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061T (9 August 2018); https://doi.org/10.1117/12.2503280

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Principal component analysis

Databases

Facial recognition systems

Detection and tracking algorithms

Error control coding

Feature extraction

Pattern recognition

Show All Keywords

Keywords/Phrases

Search In:

Publication Years